Today's scientific simulations are producing vast volumes of data that cannot be stored and
transferred efficiently because of limited storage capacity, parallel I/O bandwidth, and …

Abstract The Joint Laboratory on Extreme-Scale Computing (JLESC) was initiated at the
same time lossy compression for scientific data became an important topic for the scientific …

Scientific simulations on high-performance computing (HPC) systems can generate large
amounts of floating-point data per run. To mitigate the data storage bottleneck and lower the …

Error-bounded lossy compression is a state-of-the-art data reduction technique for HPC
applications because it not only significantly reduces storage overhead but also can retain …

Efficient error-controlled lossy compressors are becoming critical to the success of today's
large-scale scientific applications because of the ever-increasing volume of data produced …

Error-bounded lossy compression is becoming an indispensable technique for the success
of today's scientific projects with vast volumes of data produced during the simulations or …

Data stream compression attracts much attention recently due to the rise of IoT applications.
Thanks to the balanced computational power and energy consumption, asymmetric …

Non-negative matrix factorization (NMF) has proven to be a powerful unsupervised learning
method for uncovering hidden features in complex and noisy data sets with applications in …

In recent years, lossless and lossy compressors have been developed to cope with the ever
increasing volume of scientific floating point data. However not all compression techniques …

The Data Processing Unit (DPU)(ie, programmable SmartNICs with System-on-Chip or SoC
cores) has emerged as a valuable supplementary resource to the host CPU. The DPU …